Artificial intelligence inferencing workload placement to minimize latency in a heterogeneous environment

Abstract

A method for managing inferencing workload placement based on latency minimization includes performing an initial workload placement of the inferencing workload to assign the inferencing workload to a first production environment of the plurality of production environments, after performing the initial workload placement, monitoring: execution of the inferencing workload on the first production environment, and communication between the first production environment and a front-end environment, to obtain telemetry data associated with the execution and the communication, performing a latency analysis using the telemetry data to generate a placement recommendation, making a determination that the placement recommendation specifies a second production environment of the plurality of production environments, and based on the determination, initiating deployment of the inferencing workload to the second production environment.

What is claimed is: 
     
         1 . A method for managing workload placement, the method comprising:
 obtaining, by a workload placement service, a request for assigning an inferencing workload to one of a plurality of production environments based on latency minimization;   in response to the request:
 performing an initial workload placement of the inferencing workload to assign the inferencing workload to a first production environment of the plurality of production environments; 
 after performing the initial workload placement, monitoring:
 execution of the inferencing workload on the first production environment, and 
 communication between the first production environment and a front-end environment, to obtain telemetry data associated with the execution and the communication; 
 
 performing a latency analysis using the telemetry data to generate a placement recommendation; 
 making a determination that the placement recommendation specifies a second production environment of the plurality of production environments; and 
 based on the determination, initiating deployment of the inferencing workload to the second production environment. 
   
     
     
         2 . The method of  claim 1 , wherein the inferencing workload comprises implementing a generative artificial intelligence (AI) model. 
     
     
         3 . The method of  claim 2 , wherein the front-end environment comprises a front-end device operated by a user utilizing the generative AI model to obtain an inferencing payload. 
     
     
         4 . The method of  claim 1 , wherein the latency analysis is further based on causal variables associated with latency in the communication. 
     
     
         5 . The method of  claim 4 , wherein the causal variables comprise at least one of: clock speed of a graphics processing unit (GPU) of the first production environment, a number of GPUs used for the inferencing workload in the first production environment, a second number of GPUs available in the second production environment, and latency between GPUs executing the inferencing workload. 
     
     
         6 . The method of  claim 1 , wherein the first production environment is a computing device of an on-premise environment. 
     
     
         7 . The method of  claim 1 , wherein the first production environment is a computing device of a cloud environment operatively connected to the front-end environment via a wide area network. 
     
     
         8 . A non-transitory computer readable medium comprising computer readable program code, which when executed by a computer processor enables the computer processor to perform a method for managing information handling systems, the method comprising:
 obtaining, by a workload placement service, a request for assigning an inferencing workload to one of a plurality of production environments based on latency minimization;   in response to the request:
 performing an initial workload placement of the inferencing workload to assign the inferencing workload to a first production environment of the plurality of production environments; 
 after performing the initial workload placement, monitoring:
 execution of the inferencing workload on the first production environment, and 
 communication between the first production environment and a front-end environment, to obtain telemetry data associated with the execution and the communication; 
 
 performing a latency analysis using the telemetry data to generate a placement recommendation; 
 making a determination that the placement recommendation specifies a second production environment of the plurality of production environments; and 
 based on the determination, initiating deployment of the inferencing workload to the second production environment. 
   
     
     
         9 . The non-transitory computer readable medium of  claim 8 , wherein the inferencing workload comprises implementing a generative artificial intelligence (AI) model. 
     
     
         10 . The non-transitory computer readable medium of  claim 9 , wherein the front-end environment comprises a front-end device operated by a user utilizing the generative AI model to obtain an inferencing payload. 
     
     
         11 . The non-transitory computer readable medium of  claim 8 , wherein the latency analysis is further based on causal variables associated with latency in the communication. 
     
     
         12 . The non-transitory computer readable medium of  claim 11 , wherein the causal variables comprise at least one of: clock speed of a graphics processing unit (GPU) of the first production environment, a number of GPUs used for the inferencing workload in the first production environment, a second number of GPUs available in the second production environment, and latency between GPUs executing the inferencing workload. 
     
     
         13 . The non-transitory computer readable medium of  claim 8 , wherein the first production environment is a computing device of an on-premise environment. 
     
     
         14 . The non-transitory computer readable medium of  claim 8 , wherein the first production environment is a computing device of a cloud environment operatively connected to the front-end environment via a wide area network. 
     
     
         15 . A system, comprising:
 a processor; and   memory including instructions, which when executed by the processor, perform a method comprising:
 obtaining, by a workload placement service, a request for assigning an inferencing workload to one of a plurality of production environments based on latency minimization; 
 in response to the request:
 performing an initial workload placement of the inferencing workload to assign the inferencing workload to a first production environment of the plurality of production environments; 
 after performing the initial workload placement, monitoring:
 execution of the inferencing workload on the first production environment, and 
 communication between the first production environment and a front-end environment, to obtain telemetry data associated with the execution and the communication; 
 
 performing a latency analysis using the telemetry data to generate a placement recommendation; 
 making a determination that the placement recommendation specifies a second production environment of the plurality of production environments; and 
 based on the determination, initiating deployment of the inferencing workload to the second production environment. 
 
   
     
     
         16 . The system of  claim 15 , wherein the inferencing workload comprises implementing a generative artificial intelligence (AI) model. 
     
     
         17 . The system of  claim 16 , wherein the front-end environment comprises a front-end device operated by a user utilizing the generative AI model to obtain an inferencing payload. 
     
     
         18 . The system of  claim 15 , wherein the latency analysis is further based on causal variables associated with latency in the communication, and clock speed of a graphics processing unit (GPU) of the first production environment, a number of GPUs used for the inferencing workload in the first production environment, a second number of GPUs available in the second production environment, and latency between GPUs executing the inferencing workload. 
     
     
         19 . The system of  claim 15 , wherein the first production environment is a computing device of an on-premise environment. 
     
     
         20 . The system of  claim 15 , wherein the first production environment is a computing device of a cloud environment operatively connected to the front-end environment via a wide area network.